EPBM Advance Rate Prediction Using Hybrid Feature Selection and Support Vector Regression Modeling

Shengfeng Huang; Misagh Esmaeilpour; Pooya Dastpak; Rita Sousa

doi:10.2991/978-94-6463-104-3_22

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EPBM Advance Rate Prediction Using Hybrid Feature Selection and Support Vector Regression Modeling

Authors

Shengfeng Huang¹^{, *}, Misagh Esmaeilpour¹, Pooya Dastpak¹, Rita Sousa¹

¹Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

^*Corresponding author. Email: shuang54@stevens.edu

Corresponding Author

Shengfeng Huang

Available Online 1 March 2023.

DOI: 10.2991/978-94-6463-104-3_22 How to use a DOI?
Keywords: EPBM; tunnelling performance; advance rate; hybrid feature selection; SVR
Abstract: Advance rate (AR) prediction is crucial for optimal mechanized tunneling performance. However, the type of input features used when developing AR prediction models vary greatly from study to study. In this paper, a hybrid automatic feature selection method is presented and demonstrated through the development of a support vector regression (SVR) model for AR prediction in Earth pressure balance machine (EPBM) tunnel construction. EPBM datasets are collected from a tunnel project in the city of Porto, Portugal. Irrelevant features whose values are constant for most of the time were first removed via constant and quasi-constant detection method (CQD). Sequential forward selection (SFS) was then performed to determine the best subset of features to develop the best performed model. The results showed that the SVR model successfully predicted AR using the selected features with squared correlation coefficient (R 2 ) of 0.919 and 0.884 for training and testing, respectively. The efficiency of the feature selection method is demonstrated by comparing the results of the SVR model with feature selection and the one without. It is proved that proposed method helps improve the accuracy of the predictions by 8% and 17% for training and testing, respectively.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)
Series: Atlantis Highlights in Engineering
Publication Date: 1 March 2023
ISBN: 10.2991/978-94-6463-104-3_22
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-104-3_22 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Shengfeng Huang
AU  - Misagh Esmaeilpour
AU  - Pooya Dastpak
AU  - Rita Sousa
PY  - 2023
DA  - 2023/03/01
TI  - EPBM Advance Rate Prediction Using Hybrid Feature Selection and Support Vector Regression Modeling
BT  - Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)
PB  - Atlantis Press
SP  - 253
EP  - 264
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-104-3_22
DO  - 10.2991/978-94-6463-104-3_22
ID  - Huang2023
ER  -

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